Economic and Efficient Hybrid Vehicle Fuel Economy and Emissions Modeling Using anArtificial Neural Network Colorado State University

Format:

Conference/Event

Author/Creator:

Asher, Asher, author.

Contributor:

Bradley, Thomas H.

Briggs, Will

Galang, Abril A.

Jathar, Shantanu

Johnston, Brian

Conference Name:

WCX World Congress Experience (2018-04-10 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2018

Summary:

AbstractHigh accuracy hybrid vehicle fuel consumption (FC) and emissions models used in practice today are the product of years of research, are physics based, and bear a large computational cost. However, it may be possible to replace these models with a non-physics based, higher accuracy, and computationally efficient versions. In this research, an alternative method is developed by training and testing a time series artificial neural network (ANN) using real world, on-road data for a hydraulic hybrid truck to predict instantaneous FC and emissions. Parameters affecting model fidelity were investigated including the number of neurons in the hidden layer, specific training inputs, dataset length, and hybrid system status. The results show that the ANN model was computationally faster and predicted FC within a mean absolute error of 0-0.1%. For emissions prediction the ANN model had a mean absolute error of 0-3% across CO2, CO, and NOx aggregate predicted concentrations. Overall, these results indicate that ANN models could be used for a variety of research applications due to their economic and computational benefits such as derivation of vehicle control strategies to reduce FC and emissions in modern vehicles

Notes:

Vendor supplied data

Publisher Number:

2018-01-0315

Access Restriction:

Restricted for use by site license